Simple Plot - Event - Gyro_Velocity¶
Measurement Data¶
- Velocity
Import modules¶
In [167]:
from datetime import datetime, timedelta
import pandas as pd
#%matplotlib inline
#import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
from pandas.plotting import register_matplotlib_converters
register_matplotlib_converters()
Constants relevant for the graphs:¶
In [168]:
colors=['violet','darkgreen','black','red']
#colors=['b','g','k','r']
linestyles=['--','--','--','--']
markers=[ "^", "+", '*', '*']
#markers=[ "^", "+", '*', '.'] # optional : marker=(8,2,0)
Specify file to read in¶
In [169]:
# Configure your input file here:
fileName = "./data/VCMeasure257.csv"
In [170]:
# Read CSV file into a pandas dataframe
OrigDataFrame = pd.read_csv(fileName);
df = OrigDataFrame.copy()
# Change timestamp to python datatime
df['Time'] = pd.to_datetime(df['Time'], unit='s')
df.set_index('Time',inplace=True)
df = df.reindex(columns=['Speed','Target','Percent','Error'])
# Print the first three and last three rows of measurements
df.take([0,1,2,-3,-2,-1])
Out[170]:
In [176]:
# Create a list of relevant measurement periods in the data
dfDay= [group[1] for group in df.groupby(df.index.day)]
dfHourList = []
#print("Total days of measuements:", len(dfDay))
for i in range(len(dfDay)):
dfHour = [group[1] for group in dfDay[i].groupby(dfDay[i].index.hour)]
for j in range(len(dfHour)):
#print("Day: ", i, "Hour: ", j)
dfHourList.append(dfHour[j])
# Show the range of the time stamps
print("The following hour long chunks were found in the data:")
for i in range(len(dfHourList)):
print("Month ",dfHourList[i].index[0].month,"Day ",dfHourList[i].index[0].day,"Hour ",dfHourList[i].index[0].hour)
print("Total hours", len(dfHourList))
Configure to view a particular column(s) and timeslice(s):¶
In [177]:
# Configure what you want to see:
#labeliz = ['Target']
labeliz = ['Speed','Target','Percent','Error']
#coloriz='darkblue'
coloriz=['violet','darkgreen','black','red']
markeriz='+'
lsiz='--'
#dfHourListiz=[dfHourList[0]] # To see specific hours change this to be = dfHourList
dfHourListiz=dfHourList # To see them all hours change this to be = dfHourList
resample=0 # 0 or 1
In [178]:
for i in range(len(dfHourListiz)):
fig=plt.figure(figsize=(30,10))
ax=fig.add_subplot(111)
for j in range(len(labeliz)):
if resample==0:
ax.plot(dfHourList[i].index,dfHourList[i][labeliz[j]],c=coloriz[j],marker=markeriz,ls=lsiz,label=labeliz[j],fillstyle='none')
elif resample==1:
ax.plot(dfHourList[i].resample('s').mean().index,dfHourList[i][labels[j]].resample('s').mean(),c=coloriz[j],marker=markeriz,ls=lsiz,label=labeliz[j],fillstyle='none')
plt.legend(loc="upper left")
plt.grid(True)
title = print("Month ",dfHourList[i].index[0].month,"Day ",dfHourList[i].index[0].day,"Hour",dfHourList[i].index[0].hour)
ax.set_title(title)
plt.show()
Exponential Smoothing - Just to see what we get¶
In [179]:
def exponential_smoothing(series, alpha):
"""
series - dataset with timestamps
alpha - float [0.0, 1.0], smoothing parameter
"""
result = [series[0]] # first value is same as series
for n in range(1, len(series)):
result.append(alpha * series[n] + (1 - alpha) * result[n-1])
return result
In [180]:
#Color, marker and linestyle are defined on top of the file. If you want to change the settings define them here.
#For example:
#colors=['violet','darkgreen','black','red']
#linestyles=['--','--','--','--']
#markers=[ "^", "+", '*', '*']
def plotExponentialSmoothing_methods(series, label, alphas):
"""
Plots exponential smoothing with different alphas
series - dataset with timestamps
alphas - list of floats, smoothing parameters
"""
j=0
for alpha in alphas:
ax.plot(exponential_smoothing(series, alpha), c=colors[j],marker=markers[j],ls=linestyles[j],label="Alpha {}".format(alpha))
j = j+1
ax.plot(series.values, "c", label = "Actual")
Configure to view a particular column(s) and timeslice(s):¶
In [181]:
#labeliz=dfHourList[0].columns # Uncomment To see all columns
labeliz = ['Target','Speed']
#dfHourListiz=dfHourList # Uncomment To see all hours
#dfHourListiz= [dfHourList[2],dfHourList[3],dfHourList[4]]
#dfHourListiz= [dfHourList[2]]
dfHourListiz= dfHourList
alphas=[0.3,0.005,0.0099]
In [182]:
#labels=labeliz
for i in range(len(dfHourListiz)):
fig=plt.figure(figsize=(30,10))
ax=fig.add_subplot(111)
for j in range(len(labeliz)):
#plotExponentialSmoothing_methods(dfHourList[i][labels[j]], labels[j], [0.3,0.005,0.0099])
#plotExponentialSmoothing_methods(dfHourListiz[i][labeliz[j]], labeliz[j], [0.3,0.005,0.0099])
for alpha in alphas:
ax.plot(exponential_smoothing(dfHourListiz[i][labeliz[j]], alpha), c=colors[j],marker=markers[j],ls=linestyles[j],label="{} with Alpha {}".format(labeliz[j],alpha))
ax.plot(dfHourListiz[i][labeliz[j]].values, "y", marker='.',label = "Actual")
title = print(" Month ",dfHourListiz[i][labeliz[j]].index[0].month,"Day ",dfHourListiz[i].index[0].day,"Hour",dfHourListiz[i].index[0].hour)
ax.set_title(title)
plt.legend(loc="upper left")
plt.axis('tight')
plt.title("Exponential Smoothing")
plt.grid(True);
plt.show()
